Singapore is looking to plug a dearth of artificial intelligence (AI) skillsets in its finance sector by consolidating demand and working with stakeholders.
Citing a survey that polled 131 local financial institutions, the Monetary Authority of Singapore (MAS) said 44% of respondents deemed a shortage of AI and data analytics talent as their biggest challenge in adopting such applications.
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The central bank hopes to address this skills gap with a new initiative that aims to aggregate demand for roles and build capabilities through education institutions and training services providers.
Key players from these segments, including financial institutions, have formed a consortium and are working together to drive the initiative. These institutions include FactSet UK, National University of Singapore, Ngee Ann Polytechnic, Visa, Oversea-Chinese Banking Corporation, and United Overseas Bank.
MAS said it will aggregate skills demands through this group across various AI and data analytics roles based on the financial institution's stage of adoption in these technologies. The regulator will then work with financial institutions, institutes of higher learning, and training service providers to develop programmes to satisfy demands.
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These organizations will work together to co-curate training modules and curricula that incorporate the latest market developments and trends in AI and data analytics for applications in the financial sector. Efforts here will include developing case studies that encourage sharing of sound use cases and industry-specific data resources.
Under the new initiative, which is called the Financial Sector AI and Data Analytics (AIDA) Talent Development Programme, workgroups will assess financial institutions' stage of adoption. The workgroups will then match the organizations with training institutions that are equipped to curate programmes that are customized to meet skills demands. The consortium will offer their expertise in developing curricula for AI-specific modules, MAS said.
The group also will publish a whitepaper in the second half of the year that outlines the current AI and data analytics talent landscape in the finance sector. The document will include case studies and a skills development journey, serving as a roadmap for the development of roles in the finance industry, including details on the domain-specific and technical skillsets required. The case studies will look at key applications of AI and data analytics, including fraud monitoring, investment decisions, and compliance.
These real-world case studies will ensure practical resources are developed for training and learning, said Tan Kiat How, Singapore's Senior Minister of State for the Ministry of Communications and Information.
Tan added that collaboration between the public and private sectors will further ensure "the right interventions" are established to address current AI talent constraints.
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MAS chief fintech officer Sopnendu Mohanty said: "Supporting AI and data analytics adoption is one of our key strategies to help financial institutions evolve and adopt game-changing AI technology. However, the shortage in talent limits the industry's potential for growth."
Mohanty said Singapore aims to fuel adoption of AI and data analytics in the finance industry through the new talent development programme and equip the local workforce with "in-demand technical skills".
MAS launched a software toolkit in February 2022 that was aimed at helping financial institutions use AI responsibly. Five whitepapers were released to guide organizations on assessing their deployment based on predefined principles. According to MAS, the documents detail methodologies for incorporating the FEAT principles -- of Fairness, Ethics, Accountability, and Transparency -- into the use of AI within the financial sector.
The Singapore government last October identified AI, alongside 5G and Internet of Things, as one of the key technology trends that will drive demand for skillsets over the next three to five years. However, the government has cautioned that roles in infrastructure and operations are at risk of displacement and people will require reskilling as part of the transition toward automation and DevOps.